We present an end-to-end model using streaming physiological time series to accurately predict near-term risk for hypoxemia, a rare, but life-threatening condition known to cause serious patient harm during surgery. Inspired by the fact that a hypoxemia event is defined based on the sequence of future-observed low SpO2 (i.e., blood oxygen saturation) instances, our proposed model makes hybrid inference on both future low SpO2 instances and hypoxemia outcomes, enabled by a joint sequence autoencoder that simultaneously optimizes a discriminative decoder for label prediction, and two auxiliary decoders trained for data reconstruction and forecast, which seamlessly learns contextual latent representations that capture the transition between present state to future state. All decoders share a memory-based encoder that helps capture the global dynamics of patient measurement. For a large surgical cohort of 72,081 surgeries at a major academic medical center, our model outperforms all baselines including the model used by the state-of-the-art hypoxemia prediction system. Being able to make minute-resolution real-time prediction with clinically acceptable alarm rate to near-term hypoxemic events, particularly the more critical persistent hypoxemia, our proposed model is promising in improving clinical decision making and easing burden on perioperative care.
翻译:我们提出一个端到端模型,使用流动的生理时间序列来准确预测已知在手术期间造成严重病人伤害的罕见但危及生命的缺氧性缺血症这一稀有但有生命危险的缺血症的近期风险。受以下事实的启发,即低 SpO2 (即血液氧饱和度) 的顺序决定了缺氧性贫血现象,我们提议的模型对未来的低 SpO2 病例和缺氧性贫血结果作出混合推论,这是由联合序列自动编码器所促成的,该序列同时优化了标签预测的有区别的脱氧器,以及受过数据重建和预测培训的两名辅助脱氧器,这些分解器无缝地学习了从当前状态到未来状态之间过渡的背景潜在表现。所有脱氧性贫血现象都基于一个基于记忆的编码器,帮助捕捉到病人测量的全球动态。对于一个主要学术医疗中心的72 081个大型外科组来说,我们的模型超越了所有基线,包括最先进的缺血症预测系统所使用的模型。 能够以最短的速分辨方式实时预测,在临床可接受的临床上更精确的测测测测测测中,因此,我们最有希望的机率的机率的测测测测率的机率的机率率率率率后,在每分钟中进行每分钟的测算。